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1. Detecting Media Self-Censorship without Explicit Training Data

   https://doi.org/10.1137/1.9781611976236.62  

   Tao, Rongrong; Zhou, Baojian; Chen, Feng; Mares, Dvid; Butler, Patrick; Ramakrishnan, Naren; Kennedy, Ryan (July 2020, Proceedings of the 2020 SIAM International Conference on Data Mining)

   Demeniconi; Carlotta; Nitesh V. Chawla (Ed.)

   The motives and means of explicit state censorship have been well studied, both quantitatively and qualitatively. Self-censorship by media outlets, however, has not received nearly as much attention, mostly because it is difficult to systematically detect. We develop a novel approach to identify news media self-censorship by using social media as a sensor. We develop a hypothesis testing framework to identify and evaluate censored clusters of keywords and a near-linear-time algorithm (called GraphDPD) to identify the highest-scoring clusters as indicators of censorship. We evaluate the accuracy of our framework, versus other state-of-the-art algorithms, using both semi-synthetic and real-world data from Mexico and Venezuela during Year 2014. These tests demonstrate the capacity of our framework to identify self-censorship and provide an indicator of broader media freedom. The results of this study lay the foundation for detection, study, and policy-response to self-censorship. 

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2. Blocking-Resilient Communications in Information-Centric Networks Using Router Redirection

   https://doi.org/10.1109/GCWkshps45667.2019.9024405  

   Mozaffari, Hamid; Houmansadr, Amir; Venkataramani, Arun (December 2019, 2019 IEEE Globecom Workshops (GC Wkshps))

   Information-centric network (ICN) designs are susceptible to censorship especially packet filtering based on content names. Previous works on censorship circumvention in ICN either have high processing times or use proxies that can be blocked easily by the censoring agents. We design a new censorship circumvention approach for ICN using router redirection that enables a client in a censored region to retrieve blocked content from a censored destination without the censoring agent detecting the use of a censorship circumvention tool. We conduct ndnSIM-based simulation experiments showing that our approach is practical with only a modest end-to-end delay overhead. 

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3. Running Refraction Networking for Real

   https://doi.org/10.2478/popets-2020-0075  

   VanderSloot, Benjamin; Frolov, Sergey; Wampler, Jack; Tan, Sze Chuen; Simpson, Irv; Kallitsis, Michalis; Halderman, J. Alex; Borisov, Nikita; Wustrow, Eric (August 2020, Proceedings on Privacy Enhancing Technologies)

   null (Ed.)

   Abstract Refraction networking is a next-generation censorship circumvention approach that locates proxy functionality in the network itself, at participating ISPs or other network operators. Following years of research and development and a brief pilot, we established the world’s first production deployment of a Refraction Networking system. Our deployment uses a highperformance implementation of the TapDance protocol and is enabled as a transport in the popular circumvention app Psiphon. It uses TapDance stations at four physical uplink locations of a mid-sized ISP, Merit Network, with an aggregate bandwidth of 140 Gbps. By the end of 2019, our system was enabled as a transport option in 559,000 installations of Psiphon, and it served upwards of 33,000 unique users per month. This paper reports on our experience building the deployment and operating it for the first year. We describe how we overcame engineering challenges, present detailed performance metrics, and analyze how our system has responded to dynamic censor behavior. Finally, we review lessons learned from operating this unique artifact and discuss prospects for further scaling Refraction Networking to meet the needs of censored users. 

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4. 6SENSE: Internet-Wide IPv6 Scanning and its Security Applications

   Williams, Grant; Erdemir, Mert; Hsu, Amanda; Bhat, Shraddha; Bhaskar, Abhishek; Li, Frank; Pearce, Paul (August 2024, USENIX Security 2024)

   Internet-wide scanning is a critical tool for security researchers and practitioners alike. By exhaustively exploring the entire IPv4 address space, Internet scanning has driven the development of new security protocols, found and tracked vulnerabilities, improved DDoS defenses, and illuminated global censorship. Unfortunately, the vast scale of the IPv6 address space—340 trillion trillion trillion addresses—precludes exhaustive scanning, necessitating entirely new IPv6-specific scanning methods. As IPv6 adoption continues to grow, developing IPv6 scanning methods is vital for maintaining our capability to comprehensively investigate Internet security. We present 6SENSE, an end-to-end Internet-wide IPv6 scanning system. 6SENSE utilizes reinforcement learning coupled with an online scanner to iteratively reduce the space of possible IPv6 addresses into a tractable scannable subspace, thus discovering new IPv6 Internet hosts. 6SENSE is driven by a set of metrics we identify and define as key for evaluating the generality, diversity, and correctness of IPv6 scanning. We evaluate 6SENSE and prior generative IPv6 discovery methods across these metrics, showing that 6SENSE is able to identify tens of millions of IPv6 hosts, which compared to prior approaches, is up to 3.6x more hosts and 4x more end-site assignments, across a more diverse set of networks. From our analysis, we identify limitations in prior generative approaches that preclude their use for Internet-scale security scans. We also conduct the first Internet-wide scanning-driven security analysis of IPv6 hosts, focusing on TLS certificates unique to IPv6, surveying open ports and security-sensitive services, and identifying potential CVEs. 

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5. User Demographics and Censorship on Sina Weibo

   Kenney, W.; Leberknight, C. (January 2021, Proceedings of the 54th Hawaii International Conference on System Sciences)

   This paper investigates the relationship between demographics and the frequency of censored posts (weibos) on Sina Weibo. Our results indicate that demographics such as location, gender and paid for features do not provide a good degree of predictive power but help explain how censorship is applied on social media. Using a dataset of 226 million weibos collected in 2012, we apply a binomial regression model to evaluate the predictive quality of user demographics to identify candidates that may be targeted for censorship. Our results suggest male users who are verified (pay for mobile and security features) are more likely to be censored than females or users who are not verified. In addition, users from provinces such as Hong Kong, Macao, and Beijing are more heavily censored compared to any other province in China over the same period. 

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